Statistics on EU trade: Highlight on its heterogeneous structure and recommendations for practical applications

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Statistics on EU trade: Highlight on its

heterogeneous structure and

recommendations for practical

applications

2 0 1 2

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European Commission Joint Research Centre

Institute for the Protection and Security of the Citizen

Contact information Christophe Damerval

Address: Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy E-mail: [email protected] or [email protected] Fax: +39 0332 78 5154

http://theseus.jrc.ec.europa.eu http://ipsc.jrc.ec.europa.eu/ http://www.jrc.ec.europa.eu/

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JRC72905 EUR 25495 EN

ISBN 978-92-79-26272-2 ISSN 1831-9424 doi:10.2788/47677

Luxembourg: Publications Office of the European Union, 2012 © European Union, 2012

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Highlight on its heterogeneous structure

and recommendations for practical applications

C. Damerval, L. Orfei

Abstract

We present here an analysis of trade data, which highlights differences between Member States of the European Union, and also differences between chapters of traded products. We carry out a statistical description of quantity and value of traded products between the EU and third countries. This allows in particular to obtain specific threshold values, which are useful for filtering huge datasets. We present the results obtained on a dataset covering a wide area of trade involving the EU. These results make up a relevant source of information for specialists of antifraud and antimoney-laundering.

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1 Issues related to EU trade data 2 1.1 Motivations and applications . . . 2 1.2 EU trade data – COMEXT database . . . 2 1.3 Quality of available data . . . 3

2 Approach for statistics on EU trade data 3

2.1 Approach . . . 3 2.2 Results . . . 4 2.3 Recommendations . . . 4

3 Conclusions 4

1 Issues related to EU trade data

1.1 Motivations and applications

The identification of phenomena such as fraud and money laundering is a difficult task. To tackle it, a variety of approaches can be considered. The Joint Research Center of the European Commission contributes to this issue through the automated monitoring of trade. This trade represents vast amount of data. Relevant approaches should provide concise information on such data, allowing experts in the antifraud field to focus on specific cases.

1.2 EU trade data – COMEXT database

Here we consider EU trade data extracted from the COMEXT database. This database designed by Eurostat reports trade flows (imports and exports) between EU countries and third countries. These comprise a huge number of records, each of them containing the following information

Field Content

P: product index of a product (TARIC nomenclature) O: origin country outside EU

D: destination one of EU Member States

T: time period (month) at which trade was reported Q: quantity sum of quantities traded during the period V: value sum of values traded during the period

for imports. For exports, origin and destination are inverted (origin: one of EU Member States, destination: country outside EU). Here we focus on a dataset containing such information on a exhaustive product list (99 chapters of the TARIC nomenclature) during the period 2007-2011.

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customs, data being entered manually. This results in discrepancies, for instance extreme values. This underlines the relevance of using robust statistical methods to further process such data. Another issue is missing data, which is related to the very large number of (P,O,D,T) combinations. This is why it is important to select some relevant part of data before analysis. This is usually done by selecting only POD for which there is a satisfying number of observations, along with a thresholding performed on quantity or value.

2 Approach for statistics on EU trade data

We present here approaches to get a picture of the considered data, with a view to highlight differences between EU Member States. Results allow to better understand the structure of EU trade, and give a global picture of EU trade data. In particular we tackle the issue of thresholding values, with a view to recommend adequate values concerning each Member State and each chapter of the nomenclature. We will also study the effect of cutting-off POD with few observations, recommending an appropriate minimum number of observations to be chosen. These values will be applied to filter data before performing statistical computations.

2.1 Approach

Let us present our approach for one entity: a dataset corresponding to one of the 27 EU Member States (or the EU), one chapter of the nomenclature (or all 99 chapters), and one type of trade (imports or exports).

1. Identify the trade concerning this entity defined as the subset of all records (P,O,D,T,Q,V), where D is this entity (for instance: EU, chapter 1, imports). These records can be grouped by POD, reporting quantity Q and value V at different dates:

values (Ti, Qi, Vi) for POD (Pk, Ol, Dm) (1) Discard records corresponding to zero quantity or value

2. Compute for both Q and V

m mean σ standard deviation Q1 1st quartile Q2 median Q3 3rd quartile IQR interquartile Q3_{− Q}1

This allows to measure commonly encountered values, and to recommend adequate thresh-olds to be applied for further analysis. In addition to information focused on one entity (EU, Member State), this approach gives a basis for comparisons between Member States.

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1. Every Member State and the EU (27+1), considering all chapters together (1), distin-guishing imports and exports (2). Results are presented on Table 1 (associated with quantity) and Table 2 (associated with value). This gives information on possible values of general thresholds (one threshold for the EU or one by Member State) 2. Every Member State and the EU (27+1), every chapter (99), distinguishing imports

and exports (2). Results are presented on Tables 3-101 (each one corresponding to one chapter). These tables report statistics on trade data, ordered by chapter (99 tables, each corresponding to a chapter). Each table contains descriptive statistics on trade data (mean, standard deviation, median, quartiles, interquartile), for one chapter, each country being treated separately. This gives precise information on how to set thresholds for specific datasets (a few chapters and/or Member States for instance).

Note: value and quantity are expressed respectively in thousand Euro and tons.

2.3 Recommendations

To choose an adequate threshold, one should take into account the following elements:

• Trade data contains many outliers, which results in a very difference between mean and median. Hence it is recommended to choose a threshold based on the median, Q1 or Q3 (more generally a threshold robust to extreme values). For instance, we can choose Q1 for a small dataset, and Q3 for a large dataset.

• Thresholds can be applied on quantity or value (or both). Quantity and value are related (by the unit price), but not in a constant manner. As a consequence, these operations lead to different results.

• The numbers reported in all tables correspond to one record of trade. Since POD contain usually several records, it is important to normalize by the number of records. For instance, a thresholding rule on quantity can be expressed as

1

nb of records of one POD

X records of one POD

quantity > threshold = Q1,median,Q3, or else

3 Conclusions

In this paper we carried out an in-depth analysis of trade data. We applied a descriptive approach to a real dataset covering a wide area of trade. Results allow trade specialists to choose adequate thresholds, depending on the dataset and their needs.

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Quantity imports Quantity exports

mean std Q1 median Q3 IQR mean std Q1 median Q3 IQR

EU 358.80 13751.21 0.20 1.30 13.00 12.80 49.29 1789.39 0.00 0.40 3.90 3.90 AT 109.10 4450.80 0.10 0.50 4.50 4.40 17.38 299.45 0.00 0.10 1.10 1.10 BE 323.03 8093.35 0.20 1.90 19.20 19.00 56.64 2275.71 0.00 0.20 3.00 3.00 BG 179.04 5398.68 0.20 1.10 8.90 8.70 110.50 1861.35 0.02 0.40 4.80 4.78 CY 41.68 704.04 0.10 0.30 2.40 2.30 45.23 735.47 0.10 0.50 4.20 4.10 CZ 175.30 7214.58 0.10 0.80 7.10 7.00 14.30 189.28 0.00 0.10 1.90 1.90 DE 343.51 17596.60 0.20 1.70 16.40 16.20 36.11 859.10 0.00 0.30 3.70 3.70 DK 99.54 2566.20 0.10 0.70 6.70 6.60 21.54 679.32 0.00 0.20 1.30 1.30 EE 52.87 1210.67 0.10 0.40 3.70 3.60 52.53 819.51 0.00 0.30 3.60 3.60 ES 795.40 15171.93 0.60 4.40 26.90 26.30 91.34 1802.20 0.20 1.70 12.50 12.30 FI 360.37 10768.68 0.10 0.50 4.50 4.40 69.39 1524.94 0.00 0.10 2.20 2.20 FR 469.88 13898.11 0.30 1.90 15.00 14.70 48.52 1720.30 0.10 0.60 5.10 5.00 GB 425.79 20312.54 0.40 2.80 20.00 19.60 57.36 3971.93 0.10 0.40 3.00 2.90 GR 225.05 5513.29 0.30 2.90 19.70 19.40 145.25 1978.64 0.20 2.00 16.00 15.80 HU 142.10 6122.59 0.10 0.60 4.50 4.40 18.81 282.82 0.00 0.10 1.50 1.50 IE 121.84 3836.90 0.10 0.50 4.80 4.70 31.03 862.97 0.00 0.10 1.40 1.40 IT 680.53 19162.35 0.40 3.10 24.00 23.60 51.33 1061.19 0.20 1.40 10.00 9.80 LT 300.44 11172.04 0.10 0.70 7.60 7.50 60.44 1301.99 0.00 0.40 4.00 4.00 LU 14.03 142.94 0.10 0.20 1.20 1.10 23.49 252.25 0.00 0.00 0.30 0.30 LV 99.17 1937.83 0.10 0.50 5.00 4.90 40.81 780.51 0.00 0.20 2.50 2.50 MT 10.85 327.77 0.10 0.30 2.00 1.90 27.38 552.67 0.00 0.10 1.30 1.30 NL 638.28 20065.39 0.10 1.40 16.10 16.00 72.64 2465.58 0.00 0.30 3.10 3.10 PL 316.41 14687.45 0.10 1.10 12.40 12.30 37.64 640.83 0.00 0.30 4.10 4.10 PT 317.43 6357.56 0.10 0.60 8.20 8.10 43.64 932.53 0.00 0.20 2.50 2.50 RO 184.47 5207.46 0.10 1.10 11.10 11.00 154.53 2346.42 0.00 0.30 5.70 5.70 SE 157.33 6439.30 0.10 0.60 5.70 5.60 67.03 2098.85 0.00 0.20 2.20 2.20 SI 112.95 1996.87 0.10 0.70 7.20 7.10 17.63 318.37 0.00 0.30 2.40 2.40 SK 292.92 9088.68 0.10 0.70 6.50 6.40 28.37 463.36 0.00 0.20 3.30 3.30

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Value imports Value exports

mean std Q1 median Q3 IQR mean std Q1 median Q3 IQR

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1 Statistics on quantity traded (in tons) for each Member State and the EU,

for imports and exports (considering all 99 chapters as a whole) . . . 5

2 Statistics on value traded (in thousand Euro) for each Member State and the EU, for imports and exports (considering all 99 chapters as a whole) . 6 3 Statistics on imports and exports for chapter 1 . . . 10

4 Statistics on imports and exports for chapter 2 . . . 11

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EU 3.13 12.43 0.10 0.40 1.50 1.40 30.25 174.04 0.10 0.60 4.00 3.90 AT 0.73 1.43 0.00 0.20 0.80 0.80 53.67 103.50 0.60 5.10 64.80 64.20 BE 1.15 1.85 0.10 0.50 1.40 1.30 1.75 4.11 0.00 0.20 1.50 1.50 BG 1.96 3.51 0.20 0.60 1.70 1.50 83.29 200.94 1.10 11.00 37.75 36.65 CY 0.98 1.91 0.10 0.20 1.10 1.00 2.90 12.89 0.10 0.10 0.10 0.00 CZ 0.70 1.61 0.10 0.10 0.40 0.30 14.50 59.10 0.00 0.10 1.50 1.50 DE 1.99 8.25 0.10 0.20 0.90 0.80 43.04 175.62 0.20 1.20 5.50 5.30 DK 9.48 31.40 0.10 0.70 4.70 4.60 13.14 30.72 0.20 1.50 11.20 11.00 EE 1.26 2.49 0.10 0.30 1.30 1.20 554.08 626.66 15.00 116.40 1089.50 1074.50 ES 0.15 0.26 0.00 0.10 0.20 0.20 24.58 219.60 0.00 1.10 7.00 7.00 FI 3.44 7.22 0.20 0.70 3.10 2.90 8.05 18.12 0.00 1.20 3.70 3.70 FR 0.96 2.06 0.10 0.20 0.80 0.70 17.93 130.00 0.10 0.70 2.90 2.80 GB 5.18 13.47 0.20 0.90 3.80 3.60 2.47 7.38 0.10 0.60 1.90 1.80 GR 7.23 17.35 0.20 1.20 6.30 6.10 52.47 92.59 3.10 11.20 46.70 43.60 HU 1.79 2.97 0.10 0.50 2.10 2.00 34.44 139.49 0.10 1.50 20.00 19.90 IE 0.47 1.01 0.10 0.10 0.40 0.30 14.48 61.25 0.00 0.10 2.00 2.00 IT 1.54 3.58 0.00 0.30 1.20 1.20 2.45 5.65 0.10 0.70 2.10 2.00 LT 6.08 13.53 0.20 0.90 4.30 4.10 309.31 888.32 0.10 2.50 49.90 49.80 LV 0.85 2.15 0.00 0.10 0.90 0.90 376.97 468.83 0.50 160.35 783.30 782.80 MT 1.21 4.09 0.10 0.20 0.70 0.60 0.02 0.05 0.00 0.00 0.00 0.00 NL 587.75 6113.92 0.10 0.30 4.30 4.20 14.74 114.74 0.10 0.30 1.40 1.30 PL 4.57 23.85 0.10 0.30 1.10 1.00 108.41 369.37 1.10 7.70 71.75 70.65 PT 2.33 8.29 0.10 0.40 1.70 1.60 0.20 0.94 0.00 0.00 0.20 0.20 RO 5.01 19.66 0.10 0.30 3.05 2.95 142.78 247.94 9.10 43.00 161.90 152.80 SE 75.25 198.06 2.20 6.20 43.10 40.90 1.97 7.57 0.00 0.10 0.80 0.80 SI 3.31 11.05 0.10 0.30 1.30 1.20 52.32 165.34 0.90 10.90 31.70 30.80 SK 6.54 17.43 0.10 0.20 1.60 1.50 31.26 69.23 0.10 0.90 21.40 21.30

Value Imports Value Exports

EU 102.94 1170.56 2.90 9.47 36.95 34.05 134.79 729.46 3.11 16.58 76.54 73.43 AT 4.44 5.32 1.05 2.48 5.92 4.87 159.98 373.94 5.85 35.00 159.17 153.32 BE 56.47 93.05 5.03 15.75 57.36 52.33 81.62 269.92 2.83 10.44 47.11 44.28 BG 6.40 11.21 1.37 3.11 5.43 4.06 117.33 309.10 3.52 12.99 38.20 34.68 CY 5.03 10.86 0.72 1.37 3.67 2.95 10.36 47.35 0.10 0.20 0.61 0.51 CZ 4.56 7.48 0.38 1.74 5.37 4.99 43.29 145.06 1.64 6.43 25.92 24.28 DE 15.48 33.76 1.93 5.76 15.06 13.13 201.58 582.82 9.00 38.24 140.10 131.10 DK 114.54 253.93 3.94 19.85 117.54 113.60 118.00 177.59 9.55 44.23 158.64 149.09 EE 19.18 28.49 2.22 8.02 23.18 20.96 744.26 748.96 39.56 460.68 1371.10 1331.54 ES 3.50 4.16 0.50 1.85 4.92 4.42 95.35 451.89 3.00 8.73 57.52 54.52 FI 38.40 55.24 6.22 17.66 47.70 41.48 87.74 138.98 3.00 34.70 127.80 124.80 FR 16.82 23.95 2.48 7.63 22.68 20.20 105.62 309.80 4.82 20.46 85.10 80.28 GB 63.46 129.32 5.22 20.77 67.17 61.95 337.96 2191.68 7.49 31.04 120.98 113.49 GR 89.67 158.18 5.73 24.69 102.11 96.38 56.55 101.16 7.50 19.62 53.48 45.98 HU 18.16 22.87 4.28 10.79 23.44 19.16 116.17 365.29 2.21 21.62 90.82 88.61 IE 6.62 9.12 1.05 3.74 8.33 7.28 107.96 363.48 0.20 0.80 31.50 31.30 IT 14.02 20.74 1.30 6.15 18.81 17.51 18.43 29.69 2.55 8.20 24.80 22.25 LT 63.08 106.44 5.75 20.77 74.74 68.99 418.96 1041.01 3.60 22.04 196.87 193.27 LV 18.86 41.13 3.62 7.61 17.60 13.98 439.92 512.48 2.98 214.10 778.38 775.40 MT 26.49 99.42 2.93 7.44 20.00 17.07 0.42 0.53 0.10 0.25 0.50 0.40 NL 3282.39 9511.50 8.88 141.51 1213.76 1204.88 92.77 387.42 2.21 11.76 50.60 48.39 PL 1615.65 10715.19 12.18 57.14 267.92 255.74 224.95 492.75 8.31 40.53 178.47 170.17 PT 824.75 3979.75 10.09 62.85 323.12 313.03 3.11 9.98 0.19 0.40 2.84 2.65 RO 2012.82 6481.36 11.01 64.56 972.22 961.21 233.74 379.72 18.70 75.98 278.80 260.10 SE 100.77 355.91 1.40 7.97 39.47 38.07 26.26 64.94 0.53 3.25 17.97 17.44 SI 796.11 2587.60 4.59 38.36 270.65 266.06 85.00 280.46 3.72 20.90 46.68 42.96 SK 2079.17 6204.59 18.00 74.86 203.16 185.16 99.14 210.04 3.38 11.97 60.10 56.72

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EU 219.73 1929.30 1.50 11.20 48.00 46.50 8.72 27.99 0.10 0.50 4.50 4.40 AT 15.52 32.39 0.30 1.35 15.90 15.60 21.90 51.33 0.00 0.10 0.90 0.90 BE 39.20 64.41 2.50 16.05 48.00 45.50 3.95 10.61 0.10 0.65 2.70 2.60 BG 8.97 9.35 0.80 8.00 12.10 11.30 16.59 22.24 0.10 9.20 25.00 24.90 CZ 71.92 238.14 1.50 17.70 48.40 46.90 0.49 0.61 0.10 0.20 0.90 0.80 DE 354.13 2102.01 1.30 15.70 79.10 77.80 3.88 13.18 0.10 0.50 2.20 2.10 DK 77.18 153.56 1.70 20.00 72.00 70.30 0.68 2.97 0.00 0.00 0.20 0.20 EE 46.66 147.93 9.10 20.00 29.75 20.65 0.00 . 0.00 0.00 0.00 0.00 ES 467.12 1357.97 14.70 53.20 234.70 220.00 11.88 26.17 1.50 4.40 10.00 8.50 FI 90.14 237.97 0.90 20.00 68.80 67.90 1.48 5.18 0.10 0.55 1.10 1.00 FR 266.78 1084.21 3.50 20.00 93.90 90.40 15.06 39.01 0.20 0.80 10.90 10.70 GB 528.05 2060.53 4.00 24.10 153.35 149.35 4.54 8.91 0.20 0.95 3.35 3.15 GR 196.40 703.07 10.60 26.60 87.80 77.20 23.41 55.08 4.65 11.20 22.35 17.70 HU 82.92 249.66 5.00 20.00 50.00 45.00 1.67 4.16 0.00 0.00 0.50 0.50 IE 155.70 341.48 3.10 24.30 124.10 121.00 13.00 . 13.00 13.00 13.00 0.00 IT 524.97 2426.34 8.00 28.90 130.50 122.50 6.51 22.90 0.50 1.50 5.40 4.90 LT 103.14 265.72 14.50 25.00 75.80 61.30 4.38 5.90 0.50 1.50 4.50 4.00 LU 2.12 4.06 0.20 0.55 2.20 2.00 0.00 0.00 0.00 0.00 0.00 0.00 LV 4.68 5.92 0.55 1.85 7.90 7.35 1.86 5.05 0.00 0.00 0.20 0.20 NL 131.48 638.95 1.50 9.00 40.60 39.10 20.93 43.62 0.20 4.30 18.30 18.10 PL 48.84 95.50 0.90 4.25 53.00 52.10 1.85 4.06 0.00 0.20 1.70 1.70 PT 20.04 40.07 0.30 1.80 20.30 20.00 1.13 2.14 0.00 0.10 1.40 1.40 RO 13.79 20.26 2.10 5.30 16.00 13.90 1.97 7.27 0.00 0.00 0.10 0.10 SE 11.98 19.69 0.70 2.70 17.20 16.50 0.47 1.19 0.00 0.10 0.40 0.40 SI 14.22 25.27 0.85 5.05 19.95 19.10 2.17 8.13 0.00 0.00 0.20 0.20 SK 4.03 8.06 0.00 0.75 2.90 2.90 0.00 0.00 0.00 0.00 0.00 0.00

EU 56.56 174.75 3.52 12.53 38.41 34.89 21.78 210.91 0.32 1.71 6.35 6.03 AT 15.59 31.99 0.65 3.91 15.59 14.94 5.30 11.02 0.10 0.42 2.41 2.31 BE 38.61 79.03 4.46 15.75 37.53 33.08 4.97 8.40 0.32 1.58 5.62 5.30 BG 7.48 7.17 1.26 5.19 10.62 9.36 5.57 6.33 0.93 2.80 8.02 7.09 CZ 66.99 155.00 2.57 17.92 60.04 57.47 0.27 0.25 0.02 0.28 0.48 0.46 DE 368.02 1458.72 3.55 26.36 127.88 124.33 5.36 8.74 0.37 1.64 6.45 6.08 DK 89.11 167.19 4.93 25.91 89.92 84.99 1.89 4.62 0.07 0.30 1.67 1.60 EE 36.09 52.93 11.18 23.33 42.22 31.05 0.06 . 0.06 0.06 0.06 0.00 ES 504.78 1545.18 20.62 78.23 344.80 324.18 13.40 15.35 3.87 8.29 15.94 12.07 FI 93.76 173.96 5.28 33.01 88.73 83.45 2.24 6.22 0.25 1.17 1.82 1.57 FR 306.43 976.50 7.55 34.71 145.70 138.15 24.97 62.04 1.73 3.65 14.75 13.02 GB 475.52 1711.99 9.27 42.70 217.62 208.36 8.68 19.07 1.46 3.83 8.92 7.46 GR 225.50 582.51 12.81 42.75 148.96 136.15 20.46 27.97 1.20 10.21 24.49 23.29 HU 67.91 163.56 7.19 24.06 62.99 55.80 1.13 2.40 0.05 0.25 0.89 0.84 IE 109.47 196.74 6.33 32.35 103.71 97.38 14.70 . 14.70 14.70 14.70 0.00 IT 500.22 1793.43 13.22 52.11 201.97 188.75 6.62 9.39 1.50 3.74 7.46 5.96 LT 97.15 197.42 14.43 39.85 107.81 93.38 3.06 3.28 0.70 2.13 4.85 4.15 LU 4.45 5.79 1.02 2.15 5.02 4.00 0.07 0.05 0.03 0.06 0.09 0.06 LV 17.38 23.59 3.37 9.02 21.26 17.89 1.97 6.23 0.02 0.06 0.60 0.58 NL 287.62 1122.02 10.39 47.88 134.94 124.55 94.46 501.22 0.77 3.07 10.85 10.08 PL 90.08 145.15 9.43 31.43 108.49 99.06 2.12 5.37 0.14 0.57 2.06 1.92 PT 38.60 88.00 0.64 3.51 41.10 40.46 3.60 6.24 0.10 0.42 4.40 4.30 RO 27.59 28.16 6.99 17.90 41.17 34.18 2.18 5.96 0.03 0.27 0.67 0.64 SE 46.47 70.06 4.44 17.84 57.90 53.46 1.52 3.82 0.11 0.53 1.21 1.10 SI 14.59 16.42 2.88 10.39 21.69 18.81 0.80 2.09 0.06 0.18 0.62 0.56 SK 40.75 77.66 3.81 19.01 36.49 32.68 0.01 0.00 0.01 0.01 0.01 0.00